Preserving the reliable representation of layouts has shown to be very useful in various graphical design contexts, which typically involve highly structured and content-rich objects. One such recent intuitive understanding was established by Li et al. [1]} in their LayoutGAN, which aims to generate realistic document layouts using Generative Adversarial Networks (GANs) with a wireframe rendering layer. Zheng et. al. [2]} used a GAN-based approach to generate document layouts but their work focused mainly on content aware generation, that primarily uses the content of the document as an additional prior. To use more highly structured object generation, it is very important to focus operate on the low dimensional vectors unlike CNN's. Hence, in the most recent literature, Patil et. al. [3]} has come up with a solution called 'READ' that can make use of this highly structured positional information along with content to generate document layouts. Their recursive neural network-based resulting model architecture provided state-of-the-art results for generating synthetic layouts for 2D documents. But their solution could not be applicable for document image level analysis problems. Kang et. al. [4]} actually exploited the idea to generate synthetic data at the image level for handwritten word images.
